Publications Repository - Gdańsk University of Technology

Nowadays, the construction industry has increasingly recognized the superior performance characteristics of ultra high-performance concrete (UHPC). Known for its exceptional durability and high tensile strength, UHPC material is revolutionizing structure standards subjected to extreme environmental conditions and heavy loads. This paper explores the enhancement of UHPC with nano- and micromaterials, employing advanced machine-learning (ML) techniques to optimize the prediction of mechanical properties. Moreover, by introducing the novel extreme gradient boosting (XGBoost) improved by grey wolf optimizer (GWO) algorithm, this research represents the first integration of ML with GWO in UHPC research, significantly enhancing the accuracy of predictions for key properties such as compressive, tensile, and flexural strengths. The study investigates the impact of nanotechnology on UHPC, specifically how carbon nanotubes (CNTs) and microscale reinforcements contribute to advances in strength, durability, and resilience. These enhancements are pivotal in addressing limitations of traditional concrete, especially in high-demand construction environments. The proposed GWO-XGB model has demonstrated a remarkable ability to achieve R2 values of 98.4% and 94.8% for UHPC with nanomaterial and micromaterial, respectively, indicating very high level of accuracy in predicting mechanical properties. This model also had one of the lowest error values, demonstrating its precision and ability to minimize prediction errors. This approach significantly facilitates the testing and development of UHPC by automating the accurate determination of its mechanical properties, thereby reducing the reliance on costly and time-consuming experimental methods. Highlighting the transformative potential of combining ML with engineering science, this study offers promising avenues for innovations in construction practices.

Authors

• A. Ӧzyüksel Çiftçioğlu,
• Farzin Kazemi link open in new tab ,
• Torkan Shafighfard